Sign Language Interpreter Aptitude
03 - Signing/Interpreting Predictors Analysis
Freya Watkins
University of Birmingham20 April 2022
Setup
Load packages
library(here) # file organisation & folder location
library(tidyverse) # data wrangling & plotting
library(scales) # scales on plots
library(lme4) # for linear mixed modelsR / package versions used
R.Version() ## $platform
## [1] "x86_64-w64-mingw32"
##
## $arch
## [1] "x86_64"
##
## $os
## [1] "mingw32"
##
## $system
## [1] "x86_64, mingw32"
##
## $status
## [1] ""
##
## $major
## [1] "4"
##
## $minor
## [1] "0.5"
##
## $year
## [1] "2021"
##
## $month
## [1] "03"
##
## $day
## [1] "31"
##
## $`svn rev`
## [1] "80133"
##
## $language
## [1] "R"
##
## $version.string
## [1] "R version 4.0.5 (2021-03-31)"
##
## $nickname
## [1] "Shake and Throw"packageVersion('here')## [1] '1.0.1'packageVersion('tidyverse')## [1] '1.3.1'packageVersion('scales') ## [1] '1.1.1'packageVersion('lme4') ## [1] '1.1.27.1'Read pre-wrangled data
(see 00-wrangling-setup.Rmd script)
# here::here()
apt <- readRDS(here("data", "apt-data.rds"))
apt <- as_tibble(apt)
head(apt)## # A tibble: 6 x 48
## session group ppt selection selected continuation continue L1_bsl
## <fct> <fct> <fct> <fct> <dbl> <fct> <dbl> <dbl>
## 1 pre-degree pilot HW002 rejected 0 na 0 0
## 2 pre-degree pilot HW004 rejected 0 na 0 0
## 3 pre-degree pilot HW009 rejected 0 na 0 0
## 4 pre-degree pilot HW011 rejected 0 na 0 0
## 5 pre-degree pilot HW012 rejected 0 na 0 0
## 6 pre-degree pilot HW014 rejected 0 na 0 0
## # ... with 40 more variables: self_rating <dbl>, bsl_years <chr>, age_s1 <dbl>,
## # nback_lett <dbl>, nback_spat <dbl>, nback_comb <dbl>, corsi_bspan <dbl>,
## # corsi_score <dbl>, corsi_corr <dbl>, corsi_mspan <dbl>, kirk_ceil <dbl>,
## # kirk_raw <dbl>, kirk_acc <dbl>, kbit_ceil <dbl>, kbit_raw <dbl>,
## # kbit_acc <dbl>, dspan_mem <dbl>, dspan_corr <dbl>, dspan_time <dbl>,
## # mr2d_acc <dbl>, mr2d_rt <dbl>, mr2d_sats <dbl>, mr3d_acc <dbl>,
## # mr3d_rt <dbl>, mr3d_sats <dbl>, bis_tot <dbl>, bis_att <dbl>, ...Filter out participants who did not progress beyond interview
apt <- apt %>% filter(selection == "selected")Filter out participants with deaf family members / L1 BSL
apt <- apt %>% filter(L1_bsl != 1)Convert data from long format to wide format
apt_wide <- apt %>%
select(-comments) %>%
tidyr::pivot_wider(names_from = session,
values_from = c(nback_lett:grade_terp))Plots & correlations
# set up a theme for the plots
theme_details <- theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(colour = "black", size=0.5),
plot.title = element_text(face = "bold"),
axis.title.x = element_text(face = "bold"),
axis.title.y = element_text(face = "bold"),
axis.text.x = element_text(size=10, colour="black"),
axis.text.y = element_text(colour="black"),
legend.position = "none")BSL: predicted impact
Corsi Blocks
vs BSL Grades
# Initial visuospatial skill vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Initial visuospatial skill vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000657# Initial visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial visuospatial skill vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000256# 1st year visuospatial skill vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "1st year visuospatial skill vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "1st year Corsi Blocks score ")
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.153# 1st year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "1st year visuospatial skill vs. 2nd year BSL grades",
y = "1st year BSL grade",
x = "2nd year Corsi Blocks score ")
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0867# 2nd year visuospatial skill vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(7, 10)) +
theme_minimal() + theme_details +
labs(title = "2nd year visuospatial skill vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "2nd year Corsi Blocks score ")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00871vs BSL-SRT
Does Corsi Blocks performance relate to BSL Sentence Reproduction Task?
# Initial Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
coord_cartesian(xlim = c(6, 10)) +
theme_minimal() + theme_details +
labs(title = "Initial Corsi Blocks score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.101# 2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "2nd year Corsi Blocks score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "2nd year Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.609corsi2_srt3 <- lm(`bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating,
data = apt_wide)
summary(corsi2_srt3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## 0.50226 -1.33521 -1.85779 -3.44244 -0.01693 3.98307 2.39842 -0.23138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.3634 7.0008 -0.909 0.4051
## `corsi_corr_2nd year` 2.1591 0.7907 2.731 0.0412 *
## self_rating 0.1072 0.9523 0.113 0.9147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.793 on 5 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.6099, Adjusted R-squared: 0.4539
## F-statistic: 3.909 on 2 and 5 DF, p-value: 0.09503# 3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "3rd year Corsi Blocks score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year Corsi Blocks score")
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.301corsi3_srt3 <- lm(`bsl_srt_3rd year` ~ `corsi_corr_3rd year` +
self_rating, data = apt_wide)
summary(corsi3_srt3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `corsi_corr_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2436 -2.3397 0.2179 1.3910 5.9103
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9487 9.3428 -0.209 0.842
## `corsi_corr_3rd year` 1.6346 1.0570 1.547 0.173
## self_rating -0.1538 1.1578 -0.133 0.899
##
## Residual standard error: 3.409 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.3029, Adjusted R-squared: 0.07051
## F-statistic: 1.303 on 2 and 6 DF, p-value: 0.33882D Mental Rotation
vs BSL Grades
Does 2D Mental Rotation performance relate to BSL grades?
# Initial 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "Initial 2D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0939# Initial 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial 2D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00345# 1st year 2D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "1st year 2D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "1st year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.482# 1st year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "1st year 2D Mental Rotation score vs. 2nd year BSL grades",
y = "1st year BSL grade",
x = "2nd year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.121# 2nd year 2D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
coord_cartesian(xlim = c(-1.5, 1.5)) +
theme_minimal() + theme_details +
labs(title = "2nd year 2D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "2nd year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.265mr2d2_bsl2 <- lm(`grade_bsl_2nd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide)
summary(mr2d2_bsl2)##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.458 -7.042 2.668 5.846 7.147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.653 5.782 10.316 1.74e-05 ***
## `mr2d_sats_2nd year` 4.438 2.702 1.642 0.144
## self_rating 1.660 2.466 0.673 0.522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.14 on 7 degrees of freedom
## (26 observations deleted due to missingness)
## Multiple R-squared: 0.3094, Adjusted R-squared: 0.1121
## F-statistic: 1.568 on 2 and 7 DF, p-value: 0.2737vs BSL-SRT
Does 2D Mental Rotation performance relate to BSL Sentence Reproduction Task?
# 2nd year 2D Mental Rotation score vs. 2nd year BSL-SRT scores
# just 4 data points here
# 3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3rd year 2D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year 2D Mental Rotation score")
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.191mr2d3_srt3 <- lm(`bsl_srt_3rd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide)
summary(mr2d3_srt3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3836 -0.7359 1.0129 1.5794 3.8603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.865 2.841 3.473 0.0133 *
## `mr2d_sats_3rd year` 3.108 2.074 1.499 0.1846
## self_rating 1.057 1.149 0.920 0.3932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.438 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.2906, Adjusted R-squared: 0.05418
## F-statistic: 1.229 on 2 and 6 DF, p-value: 0.3573D Mental Rotation
vs BSL Grades
Does 3D Mental Rotation performance relate to BSL grades?
# Initial 3D Mental Rotation score vs. 1st year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "Initial 3D Mental Rotation score vs. 1st year BSL grades",
y = "1st year BSL grade",
x = "Initial 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0671mr3d0_bsl1 <- lm(`grade_bsl_1st year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide)
summary(mr3d0_bsl1)##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mr3d_sats_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.040 -5.622 -1.414 7.158 18.157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 65.4722 5.8011 11.286 2.56e-09 ***
## `mr3d_sats_pre-degree` 1.5614 1.3900 1.123 0.277
## self_rating -0.4048 2.1124 -0.192 0.850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.884 on 17 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.0691, Adjusted R-squared: -0.04042
## F-statistic: 0.6309 on 2 and 17 DF, p-value: 0.5441# Initial 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial 3D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "Initial 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000282# 2nd year 3D Mental Rotation score vs. 2nd year BSL grades
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_bsl_2nd year`)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "2nd year 3D Mental Rotation score vs. 2nd year BSL grades",
y = "2nd year BSL grade",
x = "2nd year 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.386mr3d2_bsl2 <- lm(`grade_bsl_2nd year` ~ `mr3d_sats_2nd year` +
self_rating, data = apt_wide)
summary(mr3d2_bsl2)##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mr3d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.5850 -5.8497 0.7585 3.7502 9.7940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.8296 4.9508 12.489 5.48e-07 ***
## `mr3d_sats_2nd year` 4.6447 2.0988 2.213 0.0542 .
## self_rating -0.7094 2.3738 -0.299 0.7719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.986 on 9 degrees of freedom
## (24 observations deleted due to missingness)
## Multiple R-squared: 0.3924, Adjusted R-squared: 0.2574
## F-statistic: 2.906 on 2 and 9 DF, p-value: 0.1062vs BSL-SRT
Does 3D Mental Rotation performance relate to BSL Sentence Reproduction Task?
# Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-3, 1.5)) +
theme_minimal() + theme_details +
labs(title = "Initial 3D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.369mr3d0_srt3 <- lm(`bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide)
summary(mr3d0_srt3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8469 -0.7479 -0.2005 1.9028 3.2185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.55018 2.71305 5.363 0.00172 **
## `mr3d_sats_pre-degree` 1.90099 1.05072 1.809 0.12040
## self_rating -0.02618 1.06583 -0.025 0.98120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.243 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.3692, Adjusted R-squared: 0.1589
## F-statistic: 1.756 on 2 and 6 DF, p-value: 0.2511# 2nd year 3D Mental Rotation score vs. 2nd year BSL-SRT scores
# only 4 data points here
# 3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "3rd year 3D Mental Rotation score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year 3D Mental Rotation score")
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.123MLAT Number Learning
vs BSL Grades
# Initial MLAT Number Learning Accuracy vs. BSL grades 1st year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`grade_bsl_1st year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 1st year",
x = "MLAT Number Learning Accuracy", y = "BSL grade 1st year")
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0215mlat_bsl_1 <- lm(`grade_bsl_1st year` ~ `mlat_acc_pre-degree` +
self_rating, data = apt_wide)
summary(mlat_bsl_1)##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `mlat_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.9838 -7.0550 -0.3413 7.5274 14.1245
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.7371 13.3127 4.112 0.000728 ***
## `mlat_acc_pre-degree` 8.4969 12.4720 0.681 0.504872
## self_rating 0.7031 2.3654 0.297 0.769878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.11 on 17 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.02658, Adjusted R-squared: -0.08794
## F-statistic: 0.2321 on 2 and 17 DF, p-value: 0.7954# Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`grade_bsl_2nd year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL grades 2nd year",
x = "MLAT Number Learning Accuracy", y = "BSL grade 2nd year")
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0333mlat_bsl_2 <- lm(`grade_bsl_2nd year` ~ `mlat_acc_pre-degree` +
self_rating, data = apt_wide)
summary(mlat_bsl_2)##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `mlat_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.216 -7.027 3.224 7.449 17.760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.048 16.575 2.959 0.0104 *
## `mlat_acc_pre-degree` 13.098 15.646 0.837 0.4166
## self_rating 1.405 2.991 0.470 0.6459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.33 on 14 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.04827, Adjusted R-squared: -0.08769
## F-statistic: 0.355 on 2 and 14 DF, p-value: 0.7073vs BSL-SRT
# Initial MLAT Number Learning Accuracy vs. BSL-SRT 3rd year
apt_wide %>%
filter(`mlat_acc_pre-degree` != "na") %>%
filter(`bsl_srt_3rd year` != "na") %>%
ggplot(aes(x = `mlat_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title= "Initial MLAT Number Learning Accuracy vs. BSL-SRT score 3rd year",
x = "MLAT Number Learning Accuracy")
apt_wide %>%
filter(`mlat_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`mlat_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000354BSL: no predicted impact
Kirklees Sentence Reading
vs BSL Grades
# Kirklees Sentence Reading pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(aes(color = continuation)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 1st year",
y = "1st year BSL grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.188kirk0_bsl1 <- lm(`grade_bsl_1st year` ~ `kirk_acc_pre-degree` +
self_rating, data = apt_wide)
summary(kirk0_bsl1)##
## Call:
## lm(formula = `grade_bsl_1st year` ~ `kirk_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.5382 -4.7901 0.6237 3.9859 13.0213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.187 11.757 2.738 0.0110 *
## `kirk_acc_pre-degree` 35.345 13.333 2.651 0.0135 *
## self_rating 1.318 1.356 0.972 0.3402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.713 on 26 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.2161, Adjusted R-squared: 0.1558
## F-statistic: 3.583 on 2 and 26 DF, p-value: 0.04223# Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.55, .95)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0189# Kirklees Sentence Reading 1st year vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 1st year",
y = "1st year BSL grade", x = "Kirklees Sentence Reading after 1 year")
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0417# Kirklees Sentence Reading 1st year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 1st year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading after 1 year")
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0853# Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.6, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 2nd year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Kirklees Sentence Reading 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00824vs BSL-SRT
Does Kirklees Sentence Reading performance relate to BSL Sentence Reproduction Task?
# Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.55, .95)) +
theme_minimal() + theme_details +
labs(title = "Initial Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial Kirklees Sentence Reading score")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.237kirk0_srt3 <- lm(`bsl_srt_3rd year` ~ `kirk_acc_pre-degree` +
self_rating, data = apt_wide)
summary(kirk0_srt3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `kirk_acc_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1089 -1.8031 -0.1845 2.5358 4.6470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.1142 9.3522 -0.226 0.829
## `kirk_acc_pre-degree` 16.9416 10.8402 1.563 0.169
## self_rating 0.8558 1.0985 0.779 0.466
##
## Residual standard error: 3.398 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.3071, Adjusted R-squared: 0.0761
## F-statistic: 1.329 on 2 and 6 DF, p-value: 0.3327# 2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "2nd year Kirklees Sentence Reading score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year Kirklees Sentence Reading score")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.149KBIT-2 Matrices
vs BSL Grades
# KBIT-2 Matrices pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 1st year",
y = "1st year BSL grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0115# KBIT-2 Matrices pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0134# KBIT-2 Matrices 1st year vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 1st year",
y = "1st year BSL grade", x = "KBIT-2 Matrices 1st year")
apt_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0273# KBIT-2 Matrices 1st year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 1st year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices 1st year")
apt_wide %>%
filter(`kbit_acc_1st year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0000593# KBIT-2 Matrices 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.167vs BSL-SRT
Does KBIT-2 Matrices performance relate to BSL Sentence Reproduction Task?
# Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.84, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Initial KBIT-2 Matrices score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial KBIT-2 Matrices score")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00494# 2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "2nd year KBIT-2 Matrices score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year KBIT-2 Matrices score")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0933Dual N-Back
vs BSL Grades
# Dual N-Back pre-degree vs. BSL Grades 1st year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. BSL Grades 1st year",
y = "1st year BSL grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0190# Dual N-Back pre-degree vs. BSL Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. BSL Grades 2nd year",
y = "2nd year BSL grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0101# Dual N-Back 2nd year vs. BSL Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. BSL Grades 2nd year",
y = "2nd year BSL grade", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0520vs BSL-SRT
Does Dual N-Back performance relate to BSL Sentence Reproduction Task?
# Initial Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Initial Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "Initial Dual N-Back score")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00186# 2nd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "2nd year Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "2nd year Dual N-Back score")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.218# 3rd year Dual N-Back score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "3rd year Dual N-Back score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score",
x = "3rd year Dual N-Back score")
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.122nback3_srt3 <- lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` +
self_rating, data = apt_wide)
summary(nback3_srt3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3928 -0.6371 -0.3928 0.6072 6.9485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -19.9281 38.7294 -0.515 0.625
## `nback_comb_3rd year` 46.3473 56.0801 0.826 0.440
## self_rating -0.1952 1.4719 -0.133 0.899
##
## Residual standard error: 3.82 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.1246, Adjusted R-squared: -0.1671
## F-statistic: 0.4272 on 2 and 6 DF, p-value: 0.6707Digit Span
vs BSL Grades
# Digit Span pre-degree vs. BSL grades 1st year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. BSL grades 1st year",
y = "1st year BSL grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.122# Digit Span pre-degree vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_bsl_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.141# No datapoints for Digit Span @ '1 year of study'
# Digit Span 2nd year vs. BSL grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs BSL grades 2nd year",
y = "2nd year BSL grade", x = "Digit Span 2nd year")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0796vs BSL-SRT
Does Digit Span score relate to BSL-SRT score?
# Initial Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Initial Digit Span score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "Initial Digit Span score")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00124# 2nd year Digit Span score vs. 3rd year BSL-SRT scores
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "2nd year Digit Span score vs. 3rd year BSL-SRT scores",
y = "3rd year BSL-SRT score", x = "2nd year Digit Span score")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00433Barratt Impulsiveness Scale
vs BSL Grades
# Barratt Impulsiveness Scale 2nd year vs BSL grades 1st year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_bsl_1st year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity vs. BSL grades 1st year",
y = "1st year BSL grade", x = "BIS 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_bsl_1st year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_bsl_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0468# Barratt Impulsiveness Scale 2nd year vs BSL grades 2nd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_bsl_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_bsl_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity vs. BSL grades 2nd year",
y = "2nd year BSL grade", x = "BIS 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_bsl_2nd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_bsl_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00858vs BSL-SRT
# Barratt Impulsiveness Scale 2nd year vs BSL-SRT 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`bsl_srt_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `bsl_srt_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. BSL-SRT 3rd year",
y = "3rd year BSL-SRT score", x = "BIS Score 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`bsl_srt_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`bsl_srt_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.272bis2_srt3 <- lm(`bsl_srt_3rd year` ~ `bis_tot_2nd year` +
self_rating, data = apt_wide)
summary(bis2_srt3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `bis_tot_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7743 -1.2297 0.4997 2.5887 4.0756
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.0529 9.3110 2.691 0.036 *
## `bis_tot_2nd year` -0.1815 0.1248 -1.454 0.196
## self_rating -0.2987 1.2260 -0.244 0.816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.466 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.2791, Adjusted R-squared: 0.03883
## F-statistic: 1.162 on 2 and 6 DF, p-value: 0.3746Interpreting: predicted impact
Dual N-Back
vs Interpreting Grades
# Dual N-Back pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0123# Dual N-Back pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.114# Dual N-Back 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0343vs Eng>BSL interpreting
# Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.125# Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.121# Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Dual N-Back 3rd year")
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.155nback3_e2b3 <- lm(`terp_e2b_3rd year` ~ `nback_comb_3rd year` +
self_rating, data = apt_wide)
summary(nback3_e2b3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.3649 -5.7844 -1.9770 0.6793 20.5151
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -38.624 133.735 -0.289 0.782
## `nback_comb_3rd year` 136.419 193.649 0.704 0.508
## self_rating 1.444 5.083 0.284 0.786
##
## Residual standard error: 13.19 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.1663, Adjusted R-squared: -0.1117
## F-statistic: 0.5982 on 2 and 6 DF, p-value: 0.5796vs BSL>Eng interpreting
# Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.71, .75)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back pre-degree")
apt_wide %>%
filter(`nback_comb_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0605# Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 2nd year")
apt_wide %>%
filter(`nback_comb_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.178# Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `nback_comb_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = " Dual N-Back 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Dual N-Back 3rd year")
apt_wide %>%
filter(`nback_comb_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`nback_comb_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.103Corsi Blocks
vs Interpreting Grades
# Corsi Blocks pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(8, 10)) +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0114# Corsi Blocks pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00830# Corsi Blocks 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_1st year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks 1st year")
apt_wide %>%
filter(`corsi_corr_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.164# Corsi Blocks 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Corsi Blocks 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Corsi Blocks 2nd year")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00315vs Eng>BSL interpreting
# Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(6, 10)) +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.289corsi0_e2b3 <- lm(`terp_e2b_3rd year` ~ `corsi_corr_pre-degree` +
self_rating, data = apt_wide)
summary(corsi0_e2b3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.9254 -8.4322 -0.1853 1.9101 22.9235
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.58284 29.41334 0.666 0.53
## `corsi_corr_pre-degree` 5.07783 3.98831 1.273 0.25
## self_rating -0.06448 4.75634 -0.014 0.99
##
## Residual standard error: 12.18 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.2893, Adjusted R-squared: 0.0524
## F-statistic: 1.221 on 2 and 6 DF, p-value: 0.359# Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 2nd year")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.565corsi2_e2b3 <- lm(`terp_e2b_3rd year` ~ `corsi_corr_2nd year` +
self_rating, data = apt_wide)
summary(corsi2_e2b3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `corsi_corr_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## -8.536 -1.520 2.139 -6.065 -9.681 3.519 8.615 11.529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3786 23.2375 0.059 0.9550
## `corsi_corr_2nd year` 6.5196 2.6245 2.484 0.0556 .
## self_rating 0.6451 3.1611 0.204 0.8463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.271 on 5 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.5687, Adjusted R-squared: 0.3962
## F-statistic: 3.297 on 2 and 5 DF, p-value: 0.1222# Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Corsi Blocks 3rd year")
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.132vs BSL>Eng interpreting
# Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(6, 10)) +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks pre-degree")
apt_wide %>%
filter(`corsi_corr_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0578# Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 2nd year")
apt_wide %>%
filter(`corsi_corr_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.455corsi2_b2e3 <- lm(`terp_b2e_3rd year` ~ `corsi_corr_2nd year` +
self_rating, data = apt_wide)
summary(corsi2_b2e3)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `corsi_corr_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22
## -0.3476 -0.3357 4.8847 -2.7752 -3.9221 -1.3221 3.8180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.2283 10.4934 4.310 0.0125 *
## `corsi_corr_2nd year` 2.2068 1.2131 1.819 0.1430
## self_rating -0.3604 1.5418 -0.234 0.8266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.984 on 4 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.4624, Adjusted R-squared: 0.1937
## F-statistic: 1.721 on 2 and 4 DF, p-value: 0.289# Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `corsi_corr_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = " Corsi Blocks 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Corsi Blocks 3rd year")
apt_wide %>%
filter(`corsi_corr_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`corsi_corr_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0169Digit Span
vs Interpreting Grades
# Digit Span pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.6, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0155# Digit Span pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.237dspan0_sli2 <- lm(`grade_terp_2nd year` ~ `dspan_corr_pre-degree` +
self_rating, data = apt_wide)
summary(dspan0_sli2)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.356 -4.900 2.872 4.838 16.355
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.078 20.770 1.015 0.3320
## `dspan_corr_pre-degree` 51.655 26.355 1.960 0.0758 .
## self_rating 1.479 2.344 0.631 0.5410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.655 on 11 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.2638, Adjusted R-squared: 0.1299
## F-statistic: 1.971 on 2 and 11 DF, p-value: 0.1856# Digit Span 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0207vs Eng>BSL interpreting
# Digit Span pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0926# Digit Span 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .81)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span 2nd year")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0851# Digit Span 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.7, .86)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Digit Span 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Digit Span 3rd year")
apt_wide %>%
filter(`dspan_corr_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.115vs BSL>Eng interpreting
# Digit Span pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.63, .72)) +
theme_minimal() + theme_details +
labs(title = "Digit Span pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span pre-degree")
apt_wide %>%
filter(`dspan_corr_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00697# Digit Span 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Digit Span 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span 2nd year")
apt_wide %>%
filter(`dspan_corr_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0863# Digit Span 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `dspan_corr_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title = "Digit Span 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Digit Span 3rd year")
apt_wide %>%
filter(`dspan_corr_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`dspan_corr_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0264Summarising Task
vs Interpreting Grades
# Summarising pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Summarising pre-degree")
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00243vs Eng>BSL interpreting
# Summarising pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. English to BSL interpreting 3rd year",
y = "English to BSL interpreting 3rd year", x = "Summarising pre-degree")
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0447vs BSL>Eng interpreting
# Summarising pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `summ_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Summarising pre-degree vs. BSL to English interpreting 3rd year",
y = "BSL to English interpreting 3rd year", x = "Summarising pre-degree")
apt_wide %>%
filter(`summ_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`summ_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00303Interpreting: no predicted impact
Kirklees Sentence Reading
vs Interpreting Grades
# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.48, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.160# Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.56, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.133# Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading after 1 year")
apt_wide %>%
filter(`kirk_acc_1st year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0201# Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees Sentence Reading 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "Kirklees Sentence Reading 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0690vs Eng>BSL interpreting
# Kirklees pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.56, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Kirklees pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0754# Kirklees 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "Kirklees 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0193vs BSL>Eng interpreting
# Kirklees pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.56, .93)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Kirklees pre-degree")
apt_wide %>%
filter(`kirk_acc_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0922# Kirklees 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kirk_acc_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "Kirklees 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "Kirklees 2nd year")
apt_wide %>%
filter(`kirk_acc_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kirk_acc_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000130KBIT-2 Matrices
vs Interpreting Grades
# KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.82, .95)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0419# KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0222# KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_1st year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 1st year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 1st year")
apt_wide %>%
filter(`kbit_acc_1st year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_1st year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00117# KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0234vs Eng>BSL interpreting
# KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.84, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000458# KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.000597vs BSL>Eng interpreting
# KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(.84, 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices pre-degree")
apt_wide %>%
filter(`kbit_acc_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0417# KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `kbit_acc_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
scale_x_continuous(labels = scales::percent_format(accuracy = 1L)) +
theme_minimal() + theme_details +
labs(title = "KBIT-2 Matrices 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "KBIT-2 Matrices 2nd year")
apt_wide %>%
filter(`kbit_acc_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`kbit_acc_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.01722D Mental Rotation
vs Interpreting Grades
# 2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_point(size = 2) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "2D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr2d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0129# 2D Mental Rotation 1st year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-1.5, 1.5)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "2D Mental Rotation 1st year")
apt_wide %>%
filter(`mr2d_sats_2nd year` != "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.242mr2d2_sli2 <- lm(`grade_terp_2nd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide)
summary(mr2d2_sli2)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.286 -4.392 -1.957 7.698 11.262
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.215 6.885 8.310 3.32e-05 ***
## `mr2d_sats_2nd year` 4.912 3.003 1.635 0.141
## self_rating 1.542 2.965 0.520 0.617
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.793 on 8 degrees of freedom
## (25 observations deleted due to missingness)
## Multiple R-squared: 0.2672, Adjusted R-squared: 0.08397
## F-statistic: 1.458 on 2 and 8 DF, p-value: 0.2884# 2D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-.5, 1.3)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
y = "3rd year Interpreting grade", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.388mr2d3_sli2 <- lm(`grade_terp_2nd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide)
summary(mr2d3_sli2)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.9347 -0.3614 2.2822 3.5333 5.8421
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.900 5.726 8.365 0.000159 ***
## `mr2d_sats_3rd year` 12.150 4.180 2.907 0.027085 *
## self_rating 4.276 2.316 1.846 0.114375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.931 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.6096, Adjusted R-squared: 0.4795
## F-statistic: 4.684 on 2 and 6 DF, p-value: 0.0595vs Eng>BSL interpreting
# 2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.198mr2d2_e2b3 <- lm(`terp_e2b_3rd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide)
summary(mr2d2_e2b3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## -12.4203 3.6479 -13.5813 8.6741 -5.9900 -0.5966 11.9458 8.3205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54.374 8.536 6.370 0.00141 **
## `mr2d_sats_2nd year` 6.633 4.569 1.452 0.20624
## self_rating 3.948 4.121 0.958 0.38199
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.62 on 5 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.3222, Adjusted R-squared: 0.05108
## F-statistic: 1.188 on 2 and 5 DF, p-value: 0.3782# 2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "2D Mental Rotation 3rd year")
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.180mr2d3_e2b3 <- lm(`terp_e2b_3rd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide)
summary(mr2d3_e2b3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.6489 -6.8092 0.7963 8.7048 10.6778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.221 9.204 5.131 0.00216 **
## `mr2d_sats_3rd year` 11.842 6.718 1.763 0.12840
## self_rating 5.611 3.722 1.507 0.18245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.14 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.4053, Adjusted R-squared: 0.2071
## F-statistic: 2.045 on 2 and 6 DF, p-value: 0.2103vs BSL>Eng interpreting
# 2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.602mr2d2_b2e3 <- lm(`terp_b2e_3rd year` ~ `mr2d_sats_2nd year` +
self_rating, data = apt_wide)
summary(mr2d2_b2e3)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_2nd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22
## -2.1612 0.6959 -1.4352 3.7724 -0.9446 -2.6385 2.7113
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.648 2.243 27.933 9.77e-06 ***
## `mr2d_sats_2nd year` 3.648 1.239 2.944 0.0422 *
## self_rating 1.205 1.134 1.062 0.3479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.027 on 4 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.6898, Adjusted R-squared: 0.5346
## F-statistic: 4.446 on 2 and 4 DF, p-value: 0.09625# 2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr2d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "2D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "2D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr2d_sats_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr2d_sats_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.311mr2d3_b2e3 <- lm(`terp_b2e_3rd year` ~ `mr2d_sats_3rd year` +
self_rating, data = apt_wide)
summary(mr2d3_b2e3)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr2d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## -3.0760 2.8403 -0.2463 2.5299 -1.6912 -5.1393 2.8670 1.9156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.032 3.008 19.955 5.84e-06 ***
## `mr2d_sats_3rd year` 4.599 2.177 2.112 0.0884 .
## self_rating 1.772 1.276 1.389 0.2235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.61 on 5 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.5025, Adjusted R-squared: 0.3035
## F-statistic: 2.525 on 2 and 5 DF, p-value: 0.17463D Mental Rotation
vs Interpreting Grades
# 3D Mental Rotation pre-degree vs. Interpreting Grades 1st year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_1st year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-2, 1.5)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation pre-degree vs. Interpreting Grades 1st year",
y = "1st year Interpreting grade", x = "3D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_1st year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_terp_1st year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0677# 3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `grade_terp_2nd year`)) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
geom_point(size = 2) +
theme_minimal() + theme_details +
labs(title= "3D Mental Rotation pre-degree vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation pre-degree") 
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.244# 3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title= "3D Mental Rotation 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation 2nd year") 
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.314# 3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title= "3D Mental Rotation 3rd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "3D Mental Rotation 3rd year") 
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.542mr3d3_sli2 <- lm(`grade_terp_2nd year` ~ `mr3d_sats_3rd year` +
self_rating, data = apt_wide)
summary(mr3d3_sli2)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `mr3d_sats_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.8513 -3.6800 -0.8694 5.5280 8.2989
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.137 5.605 8.766 0.000122 ***
## `mr3d_sats_3rd year` 7.181 2.524 2.845 0.029386 *
## self_rating 2.064 2.219 0.930 0.388267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.018 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.5997, Adjusted R-squared: 0.4662
## F-statistic: 4.494 on 2 and 6 DF, p-value: 0.06416vs Eng>BSL interpreting
# 3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-2.7, 1.2)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation pre-degree vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.268mr3d0_e2b3 <- lm(`terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide)
summary(mr3d0_e2b3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.243 -5.679 1.404 2.243 18.096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.273 10.114 6.157 0.000842 ***
## `mr3d_sats_pre-degree` 5.158 3.917 1.317 0.235955
## self_rating 2.062 3.973 0.519 0.622317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.09 on 6 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.2997, Adjusted R-squared: 0.06625
## F-statistic: 1.284 on 2 and 6 DF, p-value: 0.3435# 3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-1.2, 2.2)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 2nd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0974# 3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 3rd year vs. Eng to BSL interpreting 3rd year",
y = "Eng to BSL interpreting 3rd year", x = "3D Mental Rotation 3rd year")
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.00752vs BSL>Eng interpreting
# 3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_pre-degree`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-2.7, 1.2)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation pre-degree vs. BSL to Eng interpreting 3rd year",
y = "BSL to BSL interpreting 3rd year", x = "3D Mental Rotation pre-degree")
apt_wide %>%
filter(`mr3d_sats_pre-degree`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_pre-degree`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.475mr3d0_b2e3 <- lm(`terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` +
self_rating, data = apt_wide)
summary(mr3d0_b2e3)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## -4.4451 0.3171 0.6291 -0.3183 -1.4933 -2.9145 2.7963 5.4287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.4684 3.1264 21.260 4.27e-06 ***
## `mr3d_sats_pre-degree` 2.4042 1.1997 2.004 0.101
## self_rating 0.2235 1.2903 0.173 0.869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.698 on 5 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.4779, Adjusted R-squared: 0.2691
## F-statistic: 2.289 on 2 and 5 DF, p-value: 0.1969# 3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
coord_cartesian(xlim = c(-1.2, 2.2)) +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 2nd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr3d_sats_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.286# 3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
ggplot(aes(x = `mr3d_sats_3rd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
geom_vline(xintercept = 0, alpha = .4, linetype = "dashed") +
theme_minimal() + theme_details +
labs(title = "3D Mental Rotation 3rd year vs. BSL to Eng interpreting 3rd year",
y = "BSL to Eng interpreting 3rd year", x = "3D Mental Rotation 2nd year")
apt_wide %>%
filter(`mr3d_sats_3rd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`mr3d_sats_3rd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.158Barratt Impulsiveness Scale
vs Interpreting Grades
# Barratt Impulsiveness Scale 2nd year vs. Interpreting Grades 2nd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`grade_terp_2nd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `grade_terp_2nd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. Interpreting Grades 2nd year",
y = "2nd year Interpreting grade", x = "BIS score 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`grade_terp_2nd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`grade_terp_2nd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.0431vs Eng>BSL interpreting
# Barratt Impulsiveness Scale 2nd year vs. Eng to BSL interpreting 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`terp_e2b_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `terp_e2b_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. Eng to BSL interpreting 3rd year",
y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`terp_e2b_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`terp_e2b_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.115vs BSL>Eng interpreting
# Barratt Impulsiveness Scale 2nd year vs. BSL to Eng interpreting 3rd year
apt_wide %>%
filter(`bis_tot_2nd year` != "na") %>%
filter(`terp_b2e_3rd year` != "na") %>%
ggplot(aes(x = `bis_tot_2nd year`, y = `terp_b2e_3rd year`)) +
geom_point(size = 2) +
geom_smooth(method= "lm") +
theme_minimal() + theme_details +
labs(title= "Impulsivity 2nd year vs. BSL to Eng interpreting 3rd year",
y = "2nd year Interpreting grade", x = "Impulsivity 2nd year")
apt_wide %>%
filter(`bis_tot_2nd year`!= "NA") %>%
filter(`terp_b2e_3rd year` != "NA") %>%
summarize(RSq=(cor(`bis_tot_2nd year`,
`terp_b2e_3rd year`))^2)## # A tibble: 1 x 1
## RSq
## <dbl>
## 1 0.236Models
BSL Grades
What best predicts BSL grades?
( bsl_grade_mdl <- lm(grade_bsl ~ nback_comb +
mr2d_sats +
mr3d_sats +
corsi_corr +
kirk_acc +
self_rating,
data = apt) ) ##
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats +
## corsi_corr + kirk_acc + self_rating, data = apt)
##
## Coefficients:
## (Intercept) nback_comb mr2d_sats mr3d_sats corsi_corr kirk_acc
## 196.835 -198.804 -2.115 7.464 -1.193 28.125
## self_rating
## -1.652summary(bsl_grade_mdl)##
## Call:
## lm(formula = grade_bsl ~ nback_comb + mr2d_sats + mr3d_sats +
## corsi_corr + kirk_acc + self_rating, data = apt)
##
## Residuals:
## 66 69 70 73 75 77 78 80 81 86
## 9.5983 -6.8359 -2.0936 3.0103 2.9968 -4.1255 0.2713 -5.8613 3.5603 -0.5206
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 196.835 138.471 1.421 0.250
## nback_comb -198.804 166.286 -1.196 0.318
## mr2d_sats -2.115 4.727 -0.447 0.685
## mr3d_sats 7.464 4.334 1.722 0.184
## corsi_corr -1.193 3.204 -0.372 0.734
## kirk_acc 28.125 33.426 0.841 0.462
## self_rating -1.652 3.484 -0.474 0.668
##
## Residual standard error: 8.673 on 3 degrees of freedom
## (110 observations deleted due to missingness)
## Multiple R-squared: 0.664, Adjusted R-squared: -0.008091
## F-statistic: 0.988 on 6 and 3 DF, p-value: 0.5498# only fit on 10 observations
# wide version - pre-degree
( bsl_grade_mdl_2 <- lm(`grade_bsl_2nd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
`mlat_acc_pre-degree` +
self_rating,
data = apt_wide) ) ##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `mlat_acc_pre-degree` + self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## -7.0127 36.4042 -0.8714
## `corsi_corr_pre-degree` `kirk_acc_pre-degree` `mlat_acc_pre-degree`
## 0.8696 26.7125 10.5758
## self_rating
## 2.3496summary(bsl_grade_mdl_2)##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `mlat_acc_pre-degree` + self_rating, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.834 -2.578 3.819 5.891 15.540
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.0127 84.6677 -0.083 0.936
## `nback_comb_pre-degree` 36.4042 93.6533 0.389 0.706
## `mr3d_sats_pre-degree` -0.8714 2.4931 -0.350 0.734
## `corsi_corr_pre-degree` 0.8696 3.5433 0.245 0.811
## `kirk_acc_pre-degree` 26.7125 42.5877 0.627 0.545
## `mlat_acc_pre-degree` 10.5758 19.3488 0.547 0.597
## self_rating 2.3496 3.9823 0.590 0.568
##
## Residual standard error: 14.14 on 10 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.106, Adjusted R-squared: -0.4304
## F-statistic: 0.1976 on 6 and 10 DF, p-value: 0.97# final assessments in 3rd year
( bsl_grade_mdl_3 <- lm(`grade_bsl_2nd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year` +
self_rating,
data = apt_wide) ) ##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -66.525 144.872 5.334
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 3.461 6.253 -44.825
## self_rating
## -1.854summary(bsl_grade_mdl_3)##
## Call:
## lm(formula = `grade_bsl_2nd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 26 28
## 0.9065 -0.3100 0.5909 4.0307 -3.2833 1.4403 -2.0927 -1.2824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -66.525 71.800 -0.927 0.524
## `nback_comb_3rd year` 144.872 138.270 1.048 0.485
## `mr3d_sats_3rd year` 5.334 3.392 1.573 0.361
## `mr2d_sats_3rd year` 3.461 8.506 0.407 0.754
## `corsi_corr_3rd year` 6.253 2.398 2.608 0.233
## `dspan_corr_3rd year` -44.825 101.233 -0.443 0.735
## self_rating -1.854 3.168 -0.585 0.663
##
## Residual standard error: 6.033 on 1 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.9237, Adjusted R-squared: 0.4658
## F-statistic: 2.017 on 6 and 1 DF, p-value: 0.4922BSL-SRT
( bsl_srt_mdl <- lm(bsl_srt ~ nback_comb +
mr3d_sats +
corsi_corr +
self_rating,
#kirk_acc +
#kbit_acc +
#dspan_corr,
data = apt) ) ##
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr +
## self_rating, data = apt)
##
## Coefficients:
## (Intercept) nback_comb mr3d_sats corsi_corr self_rating
## -46.179130 75.472442 1.883576 0.434593 0.007539summary(bsl_srt_mdl)##
## Call:
## lm(formula = bsl_srt ~ nback_comb + mr3d_sats + corsi_corr +
## self_rating, data = apt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.772 -3.277 0.507 2.495 5.864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -46.179130 30.657088 -1.506 0.1663
## nback_comb 75.472442 38.054691 1.983 0.0786 .
## mr3d_sats 1.883576 1.289609 1.461 0.1781
## corsi_corr 0.434593 1.153233 0.377 0.7150
## self_rating 0.007539 1.147214 0.007 0.9949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.226 on 9 degrees of freedom
## (106 observations deleted due to missingness)
## Multiple R-squared: 0.4738, Adjusted R-squared: 0.2399
## F-statistic: 2.026 on 4 and 9 DF, p-value: 0.1742# only being fit on 8 or 9 observations...
# wide version - pre-degree
( bsl_srt_mdl_2 <- lm(`bsl_srt_3rd year` ~
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
self_rating,
data = apt_wide) ) ##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` +
## `kirk_acc_pre-degree` + self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `mr3d_sats_pre-degree` `corsi_corr_pre-degree`
## -6.0187 1.3889 1.2925
## `kirk_acc_pre-degree` self_rating
## 12.9361 -0.5175summary(bsl_srt_mdl_2)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` +
## `kirk_acc_pre-degree` + self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26 28
## 0.4041 -2.8902 -2.4367 0.5007 1.5638 2.2072 3.0150 -2.9032 0.5392
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.0187 13.9944 -0.430 0.689
## `mr3d_sats_pre-degree` 1.3889 1.1608 1.196 0.298
## `corsi_corr_pre-degree` 1.2925 1.0540 1.226 0.287
## `kirk_acc_pre-degree` 12.9361 11.6096 1.114 0.328
## self_rating -0.5175 1.3071 -0.396 0.712
##
## Residual standard error: 3.156 on 4 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.6017, Adjusted R-squared: 0.2034
## F-statistic: 1.511 on 4 and 4 DF, p-value: 0.3496# final assessments in 3rd year
( bsl_srt_mdl_3 <- lm(`bsl_srt_3rd year` ~ `nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
self_rating,
#`dspan_corr_3rd year`,
data = apt_wide) ) ##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + self_rating,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -52.77065 60.41687 0.09283
## `mr2d_sats_3rd year` `corsi_corr_3rd year` self_rating
## 3.64381 2.40889 -0.71772summary(bsl_srt_mdl_3)##
## Call:
## lm(formula = `bsl_srt_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + self_rating,
## data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## 0.64448 -1.15297 -0.51955 1.11033 0.48620 0.18338 1.54004 0.07878
## 28
## -2.37069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -52.77065 23.15888 -2.279 0.107
## `nback_comb_3rd year` 60.41687 30.88973 1.956 0.145
## `mr3d_sats_3rd year` 0.09283 1.08460 0.086 0.937
## `mr2d_sats_3rd year` 3.64381 1.85882 1.960 0.145
## `corsi_corr_3rd year` 2.40889 0.64130 3.756 0.033 *
## self_rating -0.71772 0.94426 -0.760 0.502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.959 on 3 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.8848, Adjusted R-squared: 0.6929
## F-statistic: 4.61 on 5 and 3 DF, p-value: 0.1192Interpreting Grades
( terp_grade_mdl <- lm(grade_terp ~ nback_spat +
mr3d_sats +
corsi_corr +
kirk_acc +
dspan_corr+
self_rating,
data = apt) )##
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + dspan_corr + self_rating, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr kirk_acc dspan_corr
## 349.1002 -328.2567 6.7148 -7.2400 61.9243 -51.8807
## self_rating
## 0.4704summary(terp_grade_mdl)##
## Call:
## lm(formula = grade_terp ~ nback_spat + mr3d_sats + corsi_corr +
## kirk_acc + dspan_corr + self_rating, data = apt)
##
## Residuals:
## 66 69 70 73 75 77 78 80 81 82
## 4.4869 0.1530 -5.3732 1.7185 -3.9075 -1.5299 0.4653 -1.6238 4.8893 1.1100
## 86
## -0.3886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 349.1002 77.5511 4.502 0.0108 *
## nback_spat -328.2567 78.3829 -4.188 0.0138 *
## mr3d_sats 6.7148 1.6238 4.135 0.0144 *
## corsi_corr -7.2400 2.0463 -3.538 0.0241 *
## kirk_acc 61.9243 17.5319 3.532 0.0242 *
## dspan_corr -51.8807 23.4962 -2.208 0.0918 .
## self_rating 0.4704 1.8686 0.252 0.8137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.943 on 4 degrees of freedom
## (109 observations deleted due to missingness)
## Multiple R-squared: 0.9066, Adjusted R-squared: 0.7666
## F-statistic: 6.475 on 6 and 4 DF, p-value: 0.04601# but is only being fit on 10 observations.....
# wide version - pre-degree
( terp_grade_mdl_2 <- lm(`grade_terp_2nd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`kirk_acc_pre-degree` +
`dspan_corr_pre-degree`,
data = apt_wide) )##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `dspan_corr_pre-degree`, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## -17.067 39.109 1.923
## `corsi_corr_pre-degree` `kirk_acc_pre-degree` `dspan_corr_pre-degree`
## 2.730 39.300 -2.027summary(terp_grade_mdl_2)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `kirk_acc_pre-degree` +
## `dspan_corr_pre-degree`, data = apt_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.5754 -1.6045 0.2377 3.1024 11.7750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.067 47.712 -0.358 0.730
## `nback_comb_pre-degree` 39.109 58.510 0.668 0.523
## `mr3d_sats_pre-degree` 1.923 1.427 1.348 0.215
## `corsi_corr_pre-degree` 2.730 2.848 0.959 0.366
## `kirk_acc_pre-degree` 39.300 47.544 0.827 0.432
## `dspan_corr_pre-degree` -2.027 42.627 -0.048 0.963
##
## Residual standard error: 8.88 on 8 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.5471, Adjusted R-squared: 0.264
## F-statistic: 1.933 on 5 and 8 DF, p-value: 0.1943# final assessments in 3rd year
( terp_grade_mdl_3 <- lm(`grade_terp_2nd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year` +
self_rating,
data = apt_wide) ) ##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` +
## `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` +
## `dspan_corr_3rd year` + self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -13.7316 30.6522 3.9576
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 9.0536 4.5545 -0.1829
## self_rating
## 1.4225summary(terp_grade_mdl_3)##
## Call:
## lm(formula = `grade_terp_2nd year` ~ `nback_comb_3rd year` +
## `mr3d_sats_3rd year` + `mr2d_sats_3rd year` + `corsi_corr_3rd year` +
## `dspan_corr_3rd year` + self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26 28
## 1.2492 -1.6025 -0.9810 0.9299 -1.1544 4.9851 -2.1750 -0.5637 -0.6877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.7316 51.3843 -0.267 0.814
## `nback_comb_3rd year` 30.6522 81.6926 0.375 0.744
## `mr3d_sats_3rd year` 3.9576 2.4027 1.647 0.241
## `mr2d_sats_3rd year` 9.0536 4.1523 2.180 0.161
## `corsi_corr_3rd year` 4.5545 1.7229 2.644 0.118
## `dspan_corr_3rd year` -0.1829 47.5585 -0.004 0.997
## self_rating 1.4225 2.1124 0.673 0.570
##
## Residual standard error: 4.339 on 2 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.949, Adjusted R-squared: 0.7959
## F-statistic: 6.201 on 6 and 2 DF, p-value: 0.1454BSL>Eng interpreting
( terp_b2e_mdl <- lm(terp_b2e ~ nback_spat +
mr3d_sats +
corsi_corr +
dspan_corr +
self_rating,
data = apt) )##
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr + self_rating, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr dspan_corr self_rating
## 20.065 60.044 2.279 2.615 -27.812 -1.871summary(terp_b2e_mdl)##
## Call:
## lm(formula = terp_b2e ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr + self_rating, data = apt)
##
## Residuals:
## 103 105 107 108 110 111 112 118
## -3.714 -1.343 1.808 3.084 -3.811 -2.718 5.330 1.364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.065 65.433 0.307 0.788
## nback_spat 60.044 89.747 0.669 0.572
## mr3d_sats 2.279 3.358 0.679 0.567
## corsi_corr 2.615 4.115 0.635 0.590
## dspan_corr -27.812 72.266 -0.385 0.737
## self_rating -1.871 3.927 -0.476 0.681
##
## Residual standard error: 6.346 on 2 degrees of freedom
## (112 observations deleted due to missingness)
## Multiple R-squared: 0.3849, Adjusted R-squared: -1.153
## F-statistic: 0.2503 on 5 and 2 DF, p-value: 0.9081# but is only being fit on 10 observations.....
# wide version - pre-degree
( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`dspan_corr_pre-degree`+
self_rating,
data = apt_wide) )##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## 911.589 -464.608 1.394
## `corsi_corr_pre-degree` `dspan_corr_pre-degree` self_rating
## 22.437 -1009.482 NAsummary(terp_b2e_mdl_2)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## ALL 5 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 911.589 NA NA NA
## `nback_comb_pre-degree` -464.608 NA NA NA
## `mr3d_sats_pre-degree` 1.394 NA NA NA
## `corsi_corr_pre-degree` 22.437 NA NA NA
## `dspan_corr_pre-degree` -1009.482 NA NA NA
## self_rating NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 4 and 0 DF, p-value: NA( terp_b2e_mdl_2 <- lm(`terp_b2e_3rd year` ~
`mr3d_sats_pre-degree`+
self_rating,
data = apt_wide) )##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `mr3d_sats_pre-degree` self_rating
## 66.4684 2.4042 0.2235# final assessments in 3rd year
( terp_b2e_mdl_3 <- lm(`terp_b2e_3rd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year`+
self_rating,
data = apt_wide) ) ##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## 44.2229 33.9926 -3.1494
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 8.3609 -0.1087 -8.7053
## self_rating
## 2.6857summary(terp_b2e_mdl_3)##
## Call:
## lm(formula = `terp_b2e_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 28
## -1.2305 1.8597 1.3958 0.1906 0.3307 -5.6281 2.6628 0.4190
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.2229 80.3244 0.551 0.680
## `nback_comb_3rd year` 33.9926 128.3111 0.265 0.835
## `mr3d_sats_3rd year` -3.1494 6.5776 -0.479 0.716
## `mr2d_sats_3rd year` 8.3609 8.1955 1.020 0.494
## `corsi_corr_3rd year` -0.1087 4.4991 -0.024 0.985
## `dspan_corr_3rd year` -8.7053 80.3181 -0.108 0.931
## self_rating 2.6857 4.9209 0.546 0.682
##
## Residual standard error: 6.783 on 1 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.6487, Adjusted R-squared: -1.459
## F-statistic: 0.3077 on 6 and 1 DF, p-value: 0.8785Eng>BSL interpreting
( terp_e2b_mdl <- lm(terp_e2b ~ nback_spat +
mr3d_sats +
corsi_corr +
dspan_corr+
self_rating,
data = apt) )##
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr + self_rating, data = apt)
##
## Coefficients:
## (Intercept) nback_spat mr3d_sats corsi_corr dspan_corr self_rating
## -74.3498 124.0204 0.4579 4.0471 14.3122 -1.0351summary(terp_e2b_mdl)##
## Call:
## lm(formula = terp_e2b ~ nback_spat + mr3d_sats + corsi_corr +
## dspan_corr + self_rating, data = apt)
##
## Residuals:
## 103 105 107 108 110 111 112 116
## -17.4064 -1.6379 -0.8478 -0.2062 -14.0278 0.3512 15.1243 7.4936
## 118
## 11.1570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -74.3498 177.8746 -0.418 0.704
## nback_spat 124.0204 243.5084 0.509 0.646
## mr3d_sats 0.4579 6.6724 0.069 0.950
## corsi_corr 4.0471 6.6784 0.606 0.587
## dspan_corr 14.3122 168.1128 0.085 0.938
## self_rating -1.0351 8.2803 -0.125 0.908
##
## Residual standard error: 17.44 on 3 degrees of freedom
## (111 observations deleted due to missingness)
## Multiple R-squared: 0.2709, Adjusted R-squared: -0.9442
## F-statistic: 0.223 on 5 and 3 DF, p-value: 0.9301# but is only being fit on 10 observations.....
# wide version - pre-degree
( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~
`nback_comb_pre-degree` +
`mr3d_sats_pre-degree` +
`corsi_corr_pre-degree` +
`dspan_corr_pre-degree`+
self_rating,
data = apt_wide) )##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_pre-degree` `mr3d_sats_pre-degree`
## -201.531 244.387 8.151
## `corsi_corr_pre-degree` `dspan_corr_pre-degree` self_rating
## 17.899 -17.186 -18.894summary(terp_e2b_mdl_2)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_pre-degree` +
## `mr3d_sats_pre-degree` + `corsi_corr_pre-degree` + `dspan_corr_pre-degree` +
## self_rating, data = apt_wide)
##
## Residuals:
## ALL 6 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -201.531 NA NA NA
## `nback_comb_pre-degree` 244.387 NA NA NA
## `mr3d_sats_pre-degree` 8.151 NA NA NA
## `corsi_corr_pre-degree` 17.899 NA NA NA
## `dspan_corr_pre-degree` -17.186 NA NA NA
## self_rating -18.894 NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 5 and 0 DF, p-value: NA( terp_e2b_mdl_2 <- lm(`terp_e2b_3rd year` ~
`mr3d_sats_pre-degree`+
self_rating,
data = apt_wide) )##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `mr3d_sats_pre-degree` + self_rating,
## data = apt_wide)
##
## Coefficients:
## (Intercept) `mr3d_sats_pre-degree` self_rating
## 62.273 5.158 2.062# final assessments in 3rd year
( terp_e2b_mdl_3 <- lm(`terp_e2b_3rd year` ~
`nback_comb_3rd year` +
`mr3d_sats_3rd year` +
`mr2d_sats_3rd year` +
`corsi_corr_3rd year` +
`dspan_corr_3rd year`+
self_rating,
data = apt_wide) ) ##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Coefficients:
## (Intercept) `nback_comb_3rd year` `mr3d_sats_3rd year`
## -59.757 143.253 -8.983
## `mr2d_sats_3rd year` `corsi_corr_3rd year` `dspan_corr_3rd year`
## 25.403 6.513 -62.423
## self_rating
## 4.204summary(terp_e2b_mdl_3)##
## Call:
## lm(formula = `terp_e2b_3rd year` ~ `nback_comb_3rd year` + `mr3d_sats_3rd year` +
## `mr2d_sats_3rd year` + `corsi_corr_3rd year` + `dspan_corr_3rd year` +
## self_rating, data = apt_wide)
##
## Residuals:
## 13 15 17 18 20 21 22 26
## -1.73737 3.30228 3.00420 2.93128 -1.47311 -9.67350 5.01160 -1.33559
## 28
## -0.02979
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -59.757 103.971 -0.575 0.6235
## `nback_comb_3rd year` 143.253 165.297 0.867 0.4775
## `mr3d_sats_3rd year` -8.983 4.862 -1.848 0.2059
## `mr2d_sats_3rd year` 25.403 8.402 3.024 0.0942 .
## `corsi_corr_3rd year` 6.513 3.486 1.868 0.2027
## `dspan_corr_3rd year` -62.423 96.230 -0.649 0.5831
## self_rating 4.204 4.274 0.984 0.4290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.78 on 2 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.8768, Adjusted R-squared: 0.5073
## F-statistic: 2.373 on 6 and 2 DF, p-value: 0.3259